We introduce two main modifications to the original PUnet model. First, we replace the reconstruction loss by a Dice loss [1]}, more suited to our unbalanced segmentation problem (see Section ). We weight the Dice loss so that a wrong prediction on a blend as more importance than a wrong prediction on the background, which dominates the image. Second, we set our model to train from a single ground truth. While the original PUnet model was used to cope with ambiguous segmentations, i.e images with different possible ground truths according to experts, in our case, we only have one ground truth. However, we show that the model can still capture an informative uncertainties from the diversity of the dataset.
